Statistical significance does not always mean practical importance ^77%

The Pitfall of Significance: Understanding the Difference Between Statistical and Practical Importance

As researchers, we've all been there - staring at our statistically significant results, feeling proud that we've finally achieved what we set out to do. But have you ever stopped to think about whether those results actually matter in real life? The answer is often a resounding "no".

What does statistical significance even mean?

Statistical significance refers to the probability that an observed effect or difference occurred by chance, rather than being due to some underlying relationship between variables. In other words, if we repeat our study many times, how likely are we to get similar results just by chance? When this probability is below a certain threshold (usually 0.05), we say that the result is statistically significant.

The problem with relying on statistical significance alone

The issue with relying solely on statistical significance is that it doesn't take into account the magnitude of the effect or difference being measured. A statistically significant result can be very small, and may not have any real-world impact. Here are some examples:

• A study finds a statistically significant correlation between the amount of coffee consumed and heart rate, but the increase in heart rate is only 1 beat per minute.
• Another study discovers a statistically significant difference in the average grades of students who use a new learning app versus those who don't, but the actual difference in grades is only 0.5 points.

Why does this matter?

In both cases, the results may be statistically significant, but they have little practical importance. A 1 beat per minute increase in heart rate is unlikely to cause concern for most people, and a 0.5 point difference in grades is hardly a game-changer. In fact, relying on statistical significance alone can lead to the publication of irrelevant or even misleading results.

What's the alternative?

So what should we do instead? We need to consider both the statistical significance and the practical importance of our results. This involves reporting not just the p-value (the probability that the observed effect occurred by chance), but also the size of the effect or difference being measured. This way, readers can understand whether the result has any real-world implications.

Conclusion

Statistical significance is a useful tool for researchers, but it's not enough on its own to determine the practical importance of our results. By considering both statistical significance and effect size, we can ensure that our findings are meaningful and relevant to the world beyond academia. Let's make sure to prioritize practical importance in our research, and avoid falling into the trap of relying solely on statistical significance.